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- All Subjects: artificial intelligence
- Genre: Masters Thesis
Description
Building computational models of human problem solving has been a longstanding goal in Artificial Intelligence research. The theories of cognitive architectures addressed this issue by embedding models of problem solving within them. This thesis presents an extended account of human problem solving and describes its implementation within one such theory of cognitive architecture--ICARUS. The document begins by reviewing the standard theory of problem solving, along with how previous versions of ICARUS have incorporated and expanded on it. Next it discusses some limitations of the existing mechanism and proposes four extensions that eliminate these limitations, elaborate the framework along interesting dimensions, and bring it into closer alignment with human problem-solving abilities. After this, it presents evaluations on four domains that establish the benefits of these extensions. The results demonstrate the system's ability to solve problems in various domains and its generality. In closing, it outlines related work and notes promising directions for additional research.
ContributorsTrivedi, Nishant (Author) / Langley, Patrick W (Thesis advisor) / VanLehn, Kurt (Committee member) / Kambhampati, Subbarao (Committee member) / Arizona State University (Publisher)
Created2011
Description
Natural Language Processing is a subject that combines computer science and linguistics, aiming to provide computers with the ability to understand natural language and to develop a more intuitive human-computer interaction. The research community has developed ways to translate natural language to mathematical formalisms. It has not yet been shown, however, how to automatically translate different kinds of knowledge in English to distinct formal languages. Most of the recent work presents the problem that the translation method aims to a specific formal language or is hard to generalize. In this research, I take a first step to overcome this difficulty and present two algorithms which take as input two lambda-calculus expressions G and H and compute a lambda-calculus expression F. The expression F returned by the first algorithm satisfies F@G=H and, in the case of the second algorithm, we obtain G@F=H. The lambda expressions represent the meanings of words and sentences. For each formal language that one desires to use with the algorithms, the language must be defined in terms of lambda calculus. Also, some additional concepts must be included. After doing this, given a sentence, its representation and knowing the representation of several words in the sentence, the algorithms can be used to obtain the representation of the other words in that sentence. In this work, I define two languages and show examples of their use with the algorithms. The algorithms are illustrated along with soundness and completeness proofs, the latter with respect to typed lambda-calculus formulas up to the second order. These algorithms are a core part of a natural language semantics system that translates sentences from English to formulas in different formal languages.
ContributorsAlvarez Gonzalez, Marcos (Author) / Baral, Chitta (Thesis advisor) / Lee, Joohyung (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2010
Description
Visual navigation is a useful and important task for a variety of applications. As the prevalence of robots increase, there is an increasing need for energy-efficient navigation methods as well. Many aspects of efficient visual navigation algorithms have been implemented in the literature, but there is a lack of work on evaluation of the efficiency of the image sensors. In this thesis, two methods are evaluated: adaptive image sensor quantization for traditional camera pipelines as well as new event-based sensors for low-power computer vision.The first contribution in this thesis is an evaluation of performing varying levels of sensor linear and logarithmic quantization with the task of visual simultaneous localization and mapping (SLAM). This unconventional method can provide efficiency benefits with a trade off between accuracy of the task and energy-efficiency. A new sensor quantization method, gradient-based quantization, is introduced to improve the accuracy of the task. This method only lowers the bit level of parts of the image that are less likely to be important in the SLAM algorithm since lower bit levels signify better energy-efficiency, but worse task accuracy. The third contribution is an evaluation of the efficiency and accuracy of event-based camera intensity representations for the task of optical flow. The results of performing a learning based optical flow are provided for each of five different reconstruction methods along with ablation studies. Lastly, the challenges of an event feature-based SLAM system are presented with results demonstrating the necessity for high quality and high resolution event data. The work in this thesis provides studies useful for examining tradeoffs for an efficient visual navigation system with traditional and event vision sensors. The results of this thesis also provide multiple directions for future work.
ContributorsChristie, Olivia Catherine (Author) / Jayasuriya, Suren (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022
Description
Detection of anomalies before they are included in the downstream diagnosis/prognosis models is an important criterion for maintaining the medical AI imaging model performance across internal and external datasets. Furthermore, the need to curate huge amounts of data to train supervised models that produce precise results also requires an automated model that can accurately identify in-distribution (ID) and out-of-distribution (OOD) data for ensuring the training dataset quality. However, the core challenges for designing such as system are – (i) given the infinite variations of the anomaly, curation of training data is in-feasible; (ii) making assumptions about the types of anomalies are often hypothetical. The proposed work designed an unsupervised anomaly detection model using a cascade variational autoencoder coupled with a zero-shot learning network that maps the latent vectors to semantic attributes. The performance of the proposed model is shown on two different use cases – skin images and chest radiographs and also compare against the same class of state-of-the-art generative OOD detection models.
ContributorsRamasamy, Gokul (Author) / Banerjee, Imon (Thesis advisor) / Sanyal, Arindam (Thesis advisor) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2023
Description
Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent ofdeep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more.
Despite the rise of interest in EEG signal classification, very few studies have explored the MindBigData dataset, which collects EEG signals recorded at the stimulus of seeing a digit and thinking about it. This dataset takes us closer to realizing the idea of mind-reading or communication via thought. Thus classifying these signals into the respective digit that the user thinks about is a challenging task. This serves as a motivation to study this dataset and apply existing deep learning techniques to study it.
Given the recent success of transformer architecture in different domains like Computer Vision and Natural language processing, this thesis studies transformer architecture for EEG signal classification. Also, it explores other deep learning
techniques for the same. As a result, the proposed classification pipeline achieves comparable performance with the existing methods.
ContributorsMuglikar, Omkar Dushyant (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2021
Description
Trajectory forecasting is used in many fields such as vehicle future trajectory prediction, stock market price prediction, human motion prediction and so on. Also, robots having the capability to reason about human behavior is an important aspect in human robot interaction. In trajectory prediction with regards to human motion prediction, implicit learning and reproduction of human behavior is the major challenge. This work tries to compare some of the recent advances taking a phenomenological approach to trajectory prediction. \par The work is expected to mainly target on generating future events or trajectories based on the previous data observed across many time intervals. In particular, this work presents and compares machine learning models to generate various human handwriting trajectories. Although the behavior of every individual is unique, it is still possible to broadly generalize and learn the underlying human behavior from the current observations to predict future human writing trajectories. This enables the machine or the robot to generate future handwriting trajectories given an initial trajectory from the individual thus helping the person to fill up the rest of the letter or curve. This work tests and compares the performance of Conditional Variational Autoencoders and Sinusoidal Representation Network models on handwriting trajectory prediction and reconstruction.
ContributorsKota, Venkata Anil (Author) / Ben Amor, Hani (Thesis advisor) / Venkateswara, Hemanth Kumar Demakethepalli (Committee member) / Redkar, Sangram (Committee member) / Arizona State University (Publisher)
Created2021
Description
Instructional prompts are a novel technique that can significantly improve the performance of natural language processing tasks by specifying the task instruction to the language model. This is the first paper that uses instructional prompts to improve performance of the question answering task in biomedical domain. This work makes two significant contributions. Firstly, a question answer dataset of 600K question answer pairs has been developed by using the medical textbook ‘Differential Diagnosis Primary Care’, which contains information on how to diagnose a patient by observing their disease symptoms. Secondly, a question answering language model augmented with instructional prompts has been developed by training on the medical information extracted from the book ‘Differential Diagnosis Primary Care’. Experiments have been conducted to demonstrate that it performs better than a normal question answering model that does not use instructional prompts. Instructional prompts are based on prompt tuning and prefix tuning, which are novel techniques which can help train language model to do specific downstream tasks by keeping majority of model parameters frozen, and only optimizing a small number of continuous task-specific vectors (called the prefixes).
ContributorsSaxena, Sharad (Author) / Baral, Chitta (Thesis advisor) / Blanco, Eduardo (Committee member) / Anwar, Saadat (Committee member) / Arizona State University (Publisher)
Created2021
Description
In this thesis work, a novel learning approach to solving the problem of controllinga quadcopter (drone) swarm is explored. To deal with large sizes, swarm control is
often achieved in a distributed fashion by combining different behaviors such that
each behavior implements some desired swarm characteristics, such as avoiding ob-
stacles and staying close to neighbors. One common approach in distributed swarm
control uses potential fields. A limitation of this approach is that the potential fields
often depend statically on a set of control parameters that are manually specified a
priori. This paper introduces Dynamic Potential Fields for flexible swarm control.
These potential fields are modulated by a set of dynamic control parameters (DCPs)
that can change under different environment situations. Since the focus is only on
these DCPs, it simplifies the learning problem and makes it feasible for practical use.
This approach uses soft actor critic (SAC) where the actor only determines how to
modify DCPs in the current situation, resulting in more flexible swarm control. In
the results, this work will show that the DCP approach allows for the drones to bet-
ter traverse environments with obstacles compared to several state-of-the-art swarm
control methods with a fixed set of control parameters. This approach also obtained
a higher safety score commonly used to assess swarm behavior. A basic reinforce-
ment learning approach is compared to demonstrate faster convergence. Finally, an
ablation study is conducted to validate the design of this approach.
ContributorsFerraro, Calvin Shores (Author) / Zhang, Yu (Thesis advisor) / Ben Amor, Hani (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2022
Description
The impact of Artificial Intelligence (AI) has increased significantly in daily life. AI is taking big strides towards moving into areas of life that are critical such as
healthcare but, also into areas such as entertainment and leisure. Deep neural
networks have been pivotal in making all these advancements possible. But, a well-known problem with deep neural networks is the lack of explanations for the choices
it makes. To combat this, several methods have been tried in the field of research.
One example of this is assigning rankings to the individual features and how influential
they are in the decision-making process. In contrast a newer class of methods focuses
on Concept Activation Vectors (CAV) which focus on extracting higher-level concepts
from the trained model to capture more information as a mixture of several features
and not just one. The goal of this thesis is to employ concepts in a novel domain: to
explain how a deep learning model uses computer vision to classify music into different
genres. Due to the advances in the field of computer vision with deep learning for
classification tasks, it is rather a standard practice now to convert an audio clip into
corresponding spectrograms and use those spectrograms as image inputs to the deep
learning model. Thus, a pre-trained model can classify the spectrogram images
(representing songs) into musical genres. The proposed explanation system called
“Why Pop?” tries to answer certain questions about the classification process such as
what parts of the spectrogram influence the model the most, what concepts were
extracted and how are they different for different classes. These explanations aid the
user gain insights into the model’s learnings, biases, and the decision-making process.
ContributorsSharma, Shubham (Author) / Bryan, Chris (Thesis advisor) / McDaniel, Troy (Committee member) / Sarwat, Mohamed (Committee member) / Arizona State University (Publisher)
Created2022
Description
Automated driving systems (ADS) have come a long way since their inception. It is clear that these systems rely heavily on stochastic deep learning techniques for perception, planning, and prediction, as it is impossible to construct every possible driving scenario to generate driving policies. Moreover, these systems need to be trained and validated extensively on typical and abnormal driving situations before they can be trusted with human life. However, most publicly available driving datasets only consist of typical driving behaviors. On the other hand, there is a plethora of videos available on the internet that capture abnormal driving scenarios, but they are unusable for ADS training or testing as they lack important information such as camera calibration parameters, and annotated vehicle trajectories. This thesis proposes a new toolbox, DeepCrashTest-V2, that is capable of reconstructing high-quality simulations from monocular dashcam videos found on the internet. The toolbox not only estimates the crucial parameters such as camera calibration, ego-motion, and surrounding road user trajectories but also creates a virtual world in Car Learning to Act (CARLA) using data from OpenStreetMaps to simulate the estimated trajectories. The toolbox is open-source and is made available in the form of a python package on GitHub at https://github.com/C-Aniruddh/deepcrashtest_v2.
ContributorsChandratre, Aniruddh Vinay (Author) / Fainekos, Georgios (Thesis advisor) / Ben Amor, Hani (Thesis advisor) / Pedrielli, Giulia (Committee member) / Arizona State University (Publisher)
Created2022